{"id":"W2922408548","doi":"10.3390/ijgi8030143","title":"Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Forest Service; Government of Alberta; National Aeronautics and Space Administration","keywords":"Lightning (connector); Environmental science; Vegetation (pathology); Range (aeronautics); Moderate-resolution imaging spectroradiometer; Remote sensing; Physical geography; Meteorology; Geology; Satellite; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005013846,0.0008698049,0.0003302481,0.0006713335,0.0003692892,0.0008524412,0.001263897,0.0006342237,0.0004134779],"category_scores_gemma":[0.0008649135,0.0003352309,0.0006299123,0.0005819977,0.0003709469,0.0005324561,0.0003000182,0.0003854443,0.0000937366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002200126,"about_ca_system_score_gemma":0.001945003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4324378,"about_ca_topic_score_gemma":0.307829,"domain_scores_codex":[0.9998599,0.00001998671,0.000007639169,0.00005901812,0.00002407494,0.00002928845],"domain_scores_gemma":[0.9998222,0.00006110845,0.00002927606,0.00001410047,0.00004970906,0.00002357824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004318128,0.00005748312,0.04199169,0.00001554557,0.00006844177,0.00004394013,0.00002379751,0.9512174,0.001215382,0.000166809,0.0001311866,0.005025316],"study_design_scores_gemma":[0.000007169658,0.000008526368,0.009316233,0.000002689971,0.00001060284,0.000009864646,0.0000179668,0.9901261,0.0002964428,0.00007811133,0.0001199268,0.000006302017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757203,0.0003167338,0.02093049,0.0001228341,0.00002433039,0.00003605392,0.0009431695,0.0004977983,0.001408274],"genre_scores_gemma":[0.9944476,0.00005920784,0.004692445,0.00001534763,0.000007167994,0.00001240221,0.0004707509,0.00001864358,0.0002764723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4324378,"threshold_uncertainty_score":0.8598414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057101726773971,"score_gpt":0.226737572455133,"score_spread":0.2161665551873933,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}